GitHub - Kopika0208/Multi-Class-Abnormality-Detection-in-Video-Capsule-Endoscopy-Images: Video capsule endoscopy (VCE) is an important innovation for gastroenterology and enables minimally invasive GI investigations. But VCE creates enormous amounts of data. The proposed model uses CNN architecture and ensemble learning to address this issue. And also used XAI methods like SHAP, LIME, Grad-CAM for explaining the model.

Name		Name	Last commit message	Last commit date
InceptionV3.ipynb		InceptionV3.ipynb
expai-vce.ipynb		expai-vce.ipynb
hybrid-cnn-on-vce.ipynb		hybrid-cnn-on-vce.ipynb

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Video capsule endoscopy (VCE) is an important innovation for gastroenterology and enables minimally invasive GI investigations. But VCE creates enormous amounts of data. The proposed model uses CNN architecture and ensemble learning to address this issue. And also used XAI methods like SHAP, LIME, Grad-CAM for explaining the model.

convolutional-neural-network ensemble-model gastroenterology video-capsule-endoscopy xai-evaluation

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